The readout neuron is active when the weighted sum of the inputs is above a threshold. As in the exclusive-or (XOR) problem, there is no solution if inputs include only specialized neurons that encode the pictures separately. Even in the simplest case of two pictures (A, B) and their pairs (A′, B′), Veliparib solubility dmso the readout neuron cannot respond to the two related pairs (A, A′ and B, B′) and not to the other two (A, B′ and B, A′). The solution is to add neurons that respond to nonlinear mixtures of relevant variables. The task is

solved by simply adding a third neuron that adapts its selectivity according to the cue stimulus (it discriminates A′ versus B′ only when the cue was A). In a forthcoming paper, we demonstrate that mixed selectivity in PFC neurons BKM120 has critical computational advantages (Rigotti et al., 2013). It greatly increases the complexity and number of tasks that can be learned. Rather than “confuse” downstream readout neurons, increasing the number of mixed selectivity neurons exponentially increases the number of possible input-output mappings that readout neurons can implement. Networks without mixed selectivity have a limited capacity to learn a few simple tasks. Plus, mixed selectivity speeds and eases learning because only readout neurons need to be trained and, with high-dimensional neural representations, learning

algorithms converge more rapidly (Rigotti et al., 2010). Given these advantages, it is no wonder that mixed selectivity is so widely observed in the cortex. But does mixed selectivity not create problems? Do downstream neurons not sometimes receive signals that are irrelevant or counterproductive? One solution is the oscillatory brain rhythms. They could allow neurons to communicate different messages to different targets depending on what they are synchronized with (and how, e.g., phase and frequency). For example, rat hippocampal CA1 neurons preferentially synchronize to Isotretinoin the entorhinal or CA3 neurons at different

gamma frequencies and theta phases (Colgin et al., 2009). Different frequency synchronization between human cortical areas supports recollection of spatial versus temporal information (Watrous et al., 2013). Different phases of cortical oscillations preferentially signal different pictures simultaneously held in short-term memory (Siegel et al., 2009). Monkey frontal and parietal cortices synchronize more strongly at lower versus higher frequency for top-down versus bottom-up attention, respectively (Buschman and Miller, 2007). Entraining the human frontal cortex at those frequencies produces the predicted top-down versus bottom-up effects on behavior (Chanes et al., 2013). Thus, activity from the same neurons has different functional outcomes depending on their rhythmic dynamics. For years, experimentalists have observed that cortical areas central to cognition have large proportions of “weird” neurons with mixed selectivity that cannot be pinned to one particular message.